Image processing-based robust detection of density and weaving pattern of fabric

Porosity, the measure of the open spaces within a fabric structure, is a decisive factor in the performance of textiles. It influences breathability, permeability to liquids or gases, and suitability for various industries such as apparel, medical, and technical textiles. This study compares classical porosity calculation methods with non-destructive image analysis for 24 woven fabric samples that differ in density and weave pattern. Factors such as fabric density, weave pattern, illumination conditions, magnification, and the influence of the Otsu and Yen threshold algorithms were considered. The multifactor ANOVA statistical analysis shows that fabric density and weave pattern significantly influence porosity, with illumination playing an important role, while the threshold algorithm has a minor influence. A strong correlation is found between the actual fabric porosity and the results of the image analysis, except for double-sided illumination (reflective and transmissive), where the correlation is weakest. This comprehensive investigation provides valuable insights into the reliability of different porosity assessment approaches, which is essential for applications in various textile industries.

Yarn-level reproduction of fabric appearance enables the digital generation of fabric textures, effectively replacing physical trial weaving and sampling processes and thereby reducing production labor and material waste. However, most existing methods suffer from visual distortion or high computational cost. The visual appearance of woven fabrics is influenced by multiple factors, including yarn diameter, yarn density, and weave pattern. To predict fabric appearance based on real yarns, this study proposes a weave-pattern-guided image-to-image translation framework for woven fabric appearance generation. To improve the quality of the generated fabric appearance, a multiscale group convolution (MSGC) module is introduced to capture fine-grained multiscale features. In addition, a new dataset containing diverse fabric types is constructed to provide a comprehensive basis for performance evaluation. The proposed method achieves improvements over the baseline pix2pix model, with the learned perceptual image patch similarity (LPIPS) improving by 6.02%, the structural similarity (SSIM) increasing by 0.61%, and the Fréchet inception distance (FID) improving by 30.86%. These improvements indicate that the generated appearances are visually more plausible, with enhanced perceptual fidelity and structural consistency. Experimental results demonstrate that the proposed method can effectively generate the appearance of specific woven fabrics according to given weave patterns and exhibits adaptability across different weave structures.

The weave pattern (texture) of woven fabric is considered to be an important factor of the design and production of high-quality fabric. Traditionally, the recognition of woven fabric has a lot of challenges due to its manual visual inspection. The approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and occurs more errors. Hence, an automated system is needed for classification of woven fabric to improve productivity. Along with the rapid development of computer vision, the automatic and efficient methods for woven fabric classification are desperately needed. The prediction of fabric weave pattern Fabric is done by acquiring the high-quality images of the fabric. Then the acquired images are subjected to weave classification algorithm. The output of the processed image is used as an input to the Artificial Neural Network (ANN) which uses back propagation algorithm to calculate the weighted factors and generates the desired classification of weave patterns as an output. In this review paper discussed about the study on the various neural network that are used for prediction of fabric weave pattern.

Textiles-fibres, yarns and fabrics are omnipresent in our daily lives, with unique mechanical properties that fit the design specifications for the tasks for which they are designed. The development of yarns and fabrics with negative Poisson’s ratio (NPR) is an area of current research interest due to their potential for use in high performance textiles (e.g., military, sports, etc.). The unique braiding technology of interlacement for preparation of braided helically wrapped yarns with NPR effect with later development of auxetic woven fabric made it possible to avoid the slippage of the wrapped component from the core. The applied geometrical configuration and NPR behaviour of the braided helical yarn structure with seven different angles comprising of monofilament elastomeric polyurethane (PU) core with two wrap materials that include multifilament ultra-high molecular weight polyethylene (UHMWPE) and polyethylene terephthalate (PET) fibres were investigated and analysed. The mechanically stable 2D woven textile auxetic fabrics (AF) with various weave patterns such as 2/2 matt and 3/1 twill were developed from the auxetic yarn with PU elastomer core having maximum NPR effect of −1.70 using lower wrapped angle of 9° to study and compare their mechanical responses. The auxetic yarn was used in weft direction and multifilament UHMWPE yarn in warp direction, using semi-automatic loom. Auxeticity of AF was analysed and its various mechanical properties such tensile strength, impact energy absorption, in-plane, and out-of-plane auxeticity, and puncture resistance were studied. Higher energy absorption of 84 Nm for matt fabric was seen compared to twill fabric having an energy of 65 Nm. The puncture resistance capability of matt fabric was better than twill fabric. While twill fabric exhibited better auxetic effect in both in-plane and out-of-plane mode compared to matt fabric. In short, both the twill and matt design AF’s showed unique characteristics which are beneficial in making various protective textiles such as protective helmets, bullet proof shields, cut resistance gloves, blast resistant curtains, and puncture tolerant elastomeric composites.

Fabric is an important part and parcel of our lives. In addition to fabric color and texture, fabric weave also determines the physical appearance and physical properties of the fabric. At times, it is necessary to determine the weave type of the fabric, for example, at sales outlets, or at customs for tax duty computations. Furthermore, the end-user may be required to confirm the fabric weave type. Conventionally, a manual invasive technique is used to determine the weave type of the fabric, which also requires extensive knowledge of the fabric weaving process. Therefore, in this study, we present an image-processing-based, non-invasive, low-cost fabric weave identification scheme. The experimental results are very encouraging results and show a 90% accuracy in detecting fabric weave type in real-time.

The recognition of woven fabric pattern is a crucial task for mass manufacturing and quality control in the textile industry. Traditional methods based on image processing have some limitations on accuracy and stability. In this paper, an automatic method is proposed to jointly realize yarn location and weave pattern recognition. First, a new big fabric dataset is established by a portable wireless device. The dataset contains wide kinds of fabrics and detailed fabric structure parameters. Then, a novel multi-task and multi-scale convolutional neural network (MTMSnet) is proposed to predict the location maps of yarns and floats. By adopting the multi-task structure, the MTMSnet can better learn the related features between yarns and floats. Finally, the weave pattern and basic weave repeat are recognized by combining the yarn and float location maps. Extensive experimental results on various kinds of fabrics indicate that the proposed method achieves high accuracy and quality in weave pattern recognition.

The use of nanomaterials- and nanotechnology-based processes is growing at a tremendous rate in all fields of science and technology. Textile industry is also experiencing the benefits of nanotechnology in its diverse field of applications. Textile-based nanoproducts starting from nanocomposite fibers, nanofibers to intelligent high-performance polymeric nanocoatings are getting their way not only in high performance advanced applications but nanoparticles are also successfully being used in conventional textiles to impart new functionality and improved performance. Greater repeatability, reliability and robustness are the main advantages of nanotechnological advancements in textiles. Nanoparticle application during conventional textile processing techniques, such as finishing, coating and dyeing, enhances the product performance manifold and imparts hitherto unachieved functionality. New coating techniques like sol-gel, layer-by-layer, plasma polymerization etc. can develop multi-functionality, intelligence, excellent durability and weather resistance to fabrics. The present paper focuses on the development and potential applications of nanotechnology in developing multifunctional and smart nanocomposite fibers, nanofibers and other new finished and nanocoated textiles. The four main areas of textile chemical processing, namely nanofinishing, nanocoating, nanocomposite coating and nanodyeing, are covered in the first section of this paper and the second section deals with developments in nanocomposite fibers and nanofibers. The influence of nanomaterials in textile finishing and processing to enhance product performance is discussed. Nanocoating is a relatively new technique in the textile field and is currently under research and development. Polymeric nanocomposite coatings, where nanoparticles are dispersed in polymeric media and used for coating applications, are the most promising route to develop multifunctional and intelligent high-performance textiles. Not much research has been done on applying the concept of nanotechnology in dyeing of textiles except a few reports on dye particle size reduction, structural change in fibers or the surface etching of textiles to create nanostructured surfaces. The reduction in water consumption during nanotechnology applications in textile processing has the potential to control the effluent problems of a textile process house. The most researched area to produce multifunctional, smart fibers is the preparation of nanocomposite fibers where the exceptional properties of nanoparticles have been utilized to enhance and impart several functionalities on conventional textile grade fibers. Nanofibers are gaining popularity in some specialized technical applications such as filter fabric, antibacterial patches and chemical protective suits. Nanotechnological advances in these two areas of nanocomposite fibers and nanofibrous forms have also been reviewed.

A new theory predicting the biaxial tensile properties of a triaxial-weave fabric is presented. This theory is based on the plain-weave theory developed by Kawabata, Niwa, and Kawai in 1973 and improved by Kawabata in 1984. The basic structures of the fabric are (i) the thread-spacing of two warp threads and one weft thread, (ii) the weave-crimp shrinkage of the three threads, and (iii) the crossing angles between these threads. The yarn properties applied in this theory are the tensile properties, the lateral-compressional properties, the bending property, and the yarn-crossing-torque property. The biaxial tensile properties of the triaxial-weave fabric are then derived by solving an equilibrium equation of the forces acting at a thread-crossover point. The prediction and the experimental result coincide well over the wide range of deformation from the small-strain region to the finite-deformation region near breaking strain. The theoretical analysis shows that the yarn lateral-compressional properties h...

The article presents the results of development of the woven fabric rectangular sample stretching and tearing computer simulation model. The model allows to set the sample size, elastic modulus and tensile breaking strength of the threads, random variations of these indicators for the warp and weft threads along their length. The modelling algorithm provides for the breakage of the warp threads, the redistribution of stress and strain between the sections of the threads and allows to get a detailed picture of the fabric sample deformation dynamics before it breaks. Examples of modelling results, the influence of the interaction degree between two systems of threads on the features of the strain distribution over the sections of the warp threads are given. The similarity of the simulation results according to the developed algorithm and the finite element method is shown, and the advantages of the proposed algorithm are noted.

PurposeThe purpose of this paper is to develop a computerized program that can recognize woven fabric structures and simultaneously use the obtained data to 3D re‐visualize the corresponding woven fabric structures.Design/methodology/approachA 2D bitmap image of woven fabric was initially acquired using an ordinary desktop flatbed scanner. Through several image‐processing and analysis techniques as well as recognition algorithms, the weave pattern was then identified and stored in a digital format. The weave pattern data were then used to construct warp and weft yarn paths based on Peirce's geometrical model.FindingsBy combining relevant weave parameters, including yarn sizes, warp and weft densities, yarn colours as well as cross‐sectional shapes, a 3D image of yarns assembled together as a woven fabric structure is produced and shown on a screen through the virtual reality modelling language browser.Originality/valueWoven fabric structures can now be recognised and simultaneously use the obtained data to 3D re‐visualize the corresponding woven fabric structures.

Natural silk fabric is directly derived from insect produced cocoons containing large volumes of fibroin protein. The fibroin fibres secreted by the two silk glands of Bombyx mori are smooth and soft and form the structural center of silk. Sericin is a coating with an amorphous structure that bonds the two strands. It is the component hardening the raw thread. The fabric as we know it with low mass per unit area. The level of friction of textile materials depends on a number of test factors - normal load, contact surface area, friction speed, as well as the nature of the textile surface and the direction of friction - warp - warp, warp – weft. In this study, four types of fabric made of 100% natural silk have been examined. They all have different characteristics for mass per unit area, weave, density and linear density of the warp and weft threads. The study makes use of appliance tribometer MXD -02, from Labthink, China to determine the values of static and dynamic friction coefficients. Friction is conducted in different directions and under different pressure. This experimental study has clearly showed that silk fabrics with higher surface mass - display friction coefficient values (at rest and in sliding) greater than 1. Friction index and friction coefficient are influenced by the test direction where friction index in warp direction of one fabric with parallel arrangement of the threads is higher compared to threads in weft direction of another fabric.

رؤیة تشکیلیة جدیدة للکروشیه الفیلیة على النول الیدوی والاستفادة منها فى إثراء مکملات الملابس

Women's fabrics are artworks that rely on aesthetic experience throughout the ages. women's fabrics in the Egyptian market lack the compatibility with international fashion, especially in the use of metal threads in the production of these fabrics, as well as the difficulty of operating these threads as warps The research was based on the production of (15) samples on the dobby machine, where three arrangements of metal threads and cotton threads were made in both warp and weft This is done by using five different tissue structures while keeping the count of both warp and weft, the density of the warp in the cm, and the number of picks in cm, Then laboratory tests were conducted on these samples and statistical analysis was carried out. The results of the research benefited him from the possibility of warping metal threads and using them at a rate of up to 12% of the warp and weft threads in designing women’s fabrics without affecting the properties of tensile strength and elongation in the direction of the warp or weft of the fabrics made with the research samples. There was also a non-significant effect on the hardness property of the research samples carried out under the study. Increasing the percentage of metallic threads from 3% to 12% led to an increase in the time of moisture absorption in the research samples under study

Tenun Ikat Troso, an indigenous weaving craft originating from Jepara, specifically Troso village, involves the intricate process of weaving fabric from warp and weft threads that are initially tied and dyed using natural pigments. The weaving apparatus employed for this task is the automated power loom (ATBM) (Ismanto, Tamrin, & Pebruary, 2018). The enterprising Mrs. Rahayu, who owns UMKM Yabunaya Muslim Store, embarked on her business journey in 2017. She ventured into both selling and personally manufacturing modest dresses, utilizing an assortment of Indonesian textiles, including batik prints, stamped batik (batik cap), hand-drawn batik (batik tulis), a fusion of stamped and hand-drawn batik, and handwoven fabrics created using traditional weaving tools. Mrs. Rahayu, equipped with skills acquired through private fashion courses under the guidance of industry experts at the Susan Budiharjo School (Cikini), financed her endeavors independently without any business partners. Notably, her designs remain authentically original, with no replication of other designer products. UMKM Yabunaya Muslim Store has diversified its fabric sourcing, encompassing batik fabrics from Solo, Riau, Madura, Pekalongan, Kebumen, woven fabrics from Troso, Jepara, and sasirangan fabrics from Kalimantan. The store has successfully produced approximately 60 sets of batik, Troso woven fabrics, and sasirangan dresses. Mrs. Rahayu has actively participated in exhibitions and bazaars, maintaining a strong online presence through Google My Business, while also effectively marketing her products on platforms like Instagram, Facebook, and various other social media channels.

This study presents an investigation of the effect of different structural parameters of cotton woven fabrics on their air permeability. For this purpose, 24 fabric samples having different structural properties were obtained by using three different weave types (plain, 1/3 twill and 1/7 sateen), two different weft yarn counts (Ne 20/2 and Ne 70/2) and four different yarn twist levels (120, 360, 600, and 840 turns/m). Cotton Ne 50/1; 150 turns/m warp yarns and 40 threads/cm warp density were used in all fabric samples. The relationship between the fabrics structural parameters like weft yarn count, weave type, yarn twist number and air permeability behavior are investigated.It has been shown that the increase of yarn counts and yarn twist led to an increase in air permeability values of cotton woven fabrics. Also, cotton woven fabrics with 1/7 sateen weave have the maximum air permeability value; these fabrics are followed by the fabrics having weave types of plain and 1/3 twill in spite of high weft density.